Robust Multivariate Correlation Techniques: A Confirmation Analysis using Covid-19 Data Set

Abstract

Robust multivariate correlation techniques are proposed to determine the strength of the association between two or more variables of interest since the existing multivariate correlation techniques are susceptible to outliers when the data set contains random outliers. The performances of the proposed techniques were compared with the conventional multivariate correlation techniques. All techniques under study are applied on COVID-19 data sets for Malaysia and Nigeria to determine the level of association between study variables which are confirmed, discharged, and death cases. These techniques’ performances are evaluated based on the multivariate correlation (R), multivariate coefficient of determination (R^2), and Adjusted R^2. The proposed techniques showed R=0.99 and the conventional methods showed that R ranges from 0.44 to 0.73. The R^2 and the Adjusted R^2 for proposed methods are 0.98 and 0.97 while the conventional methods showed that R equals 0.53, 0.44, and 0.19 whereas Adjusted R^2 equals 0.52, 0.43, and 0.18, respectively. The proposed techniques strongly affirmed that for any patient to be discharged or die of the Covid-19, the patient must be confirmed Covid-19 positive, whereas the conventional method showed moderate to very weak affirmation. Based on the results, the proposed techniques are robust and show a very strong association between the variables of interest than the conventional techniques.